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Accurately predicting the trajectory of carbon emissions is vital for achieving a sustainable shift toward a green and low-carbon future. Hence, this paper created a novel model to examine the driver analysis and integrated prediction for Chinese carbon emission, a large carbon-emitting country. The logarithmic mean divisia index (LMDI) approach initially served to decompose the drivers of carbon emissions, analyzing the annual and staged contributions of these factors. Given the non-stationarity and non-linear characteristics in the data sequence of carbon emissions, a decomposition–integration prediction model was proposed. The model employed the empirical mode decomposition (EMD) model to decompose each set of data into a series of components. The various carbon emission components were anticipated using the long short-term memory (LSTM) model based on the deconstructed impacting factors. The aggregate of these predicted components constituted the overall forecast for carbon emissions. The result indicates that the EMD-LSTM model greatly decreased prediction errors over the other comparable models. This paper makes up for the gap in existing research by providing further analysis based on the LMDI method. Additionally, it innovatively incorporates the EMD method into the carbon emission study, and the proposed EMD-LSTM prediction model effectively addresses the volatility characteristics of carbon emissions and demonstrates excellent predictive performance in carbon emission prediction. [ABSTRACT FROM AUTHOR]

Featured Application: This article mainly utilizes the advantages and characteristics of evolutionary game theory and based on the ideas and methods of evolutionary game theory, investigates the spontaneous formation of the relationships among the local governments, power grid enterprises, and market regulators based on low-carbon trading mechanisms during long-term carbon emission reduction. This tripartite evolution game is described as a learning progressive evolution system, focusing on the evolution process of the relationship between various stakeholders and the influencing factors of evolutionary stability in the electricity market and carbon emission market. It provides a reasonable explanation for the spontaneous formation of interest equilibrium states within the local government, power grid enterprises, and market regulators, and also provides theoretical reference and policy suggestions for government regulation to the electricity market and carbon emission market. In the context of increasing global efforts to mitigate climate change, effective carbon emission reduction is a pressing issue. Governments and power companies are key stakeholders in implementing low-carbon strategies, but their interactions require careful management to ensure optimal outcomes for both economic development and environmental protection. This paper addresses this real-world challenge by utilizing evolutionary game theory (EGT) to model the strategic interactions between these stakeholders under a low-carbon trading mechanism. Unlike classical game theory, which assumes complete rationality and perfect information, EGT allows for bounded rationality and learning over time, making it particularly suitable for modeling long-term interactions in complex systems like carbon markets. This study builds an evolutionary game model between the government and power companies to explore how different strategies in carbon emission reduction evolve over time. Using payoff matrices and replicator dynamics equations, we determine the evolutionarily stable equilibrium (ESE) points and analyze their stability through dynamic simulations. The findings show that in the absence of a third-party regulator, neither party achieves an ideal ESE. To address this, a third-party regulatory body is introduced into the model, leading to the formulation of a tripartite evolutionary game. The results highlight the importance of regulatory oversight in achieving stable and optimal low-carbon strategies. This paper offers practical policy recommendations based on the simulation outcomes, providing a robust theoretical framework for government intervention in carbon markets and guiding enterprises towards sustainable practices. [ABSTRACT FROM AUTHOR]

This paper proposes a semiparametric spatial lag model and develops a Bayesian estimation method for this model. In the estimation of the model, the paper combines Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, random walk Metropolis sampler, and Gibbs sampling techniques to sample all the parameters. The paper conducts numerical simulations to validate the proposed Bayesian estimation theory using a numerical example. The simulation results demonstrate satisfactory estimation performance of the parameter part and the fitting performance of the nonparametric function under different spatial weight matrix settings. Furthermore, the paper applies the constructed model and its estimation method to an empirical study on the relationship between economic growth and carbon emissions in China, illustrating the practical application value of the theoretical results. [ABSTRACT FROM AUTHOR]

In order to reduce pollution caused by ship emissions, the International Maritime Organization (IMO) implemented sulfur emission control areas (SECAs). In comparison to ordinary vessels, cruise ships with dual attributes of transportation and tourism generate a greater amount of marine pollution, which poses a significant threat to the marine environment in both berthing ports and the sailing area. In light of the fierce competition of the cruise tourism market, cruise lines are looking for strategies, such as designing more attractive cruise routes, to maintain their core competencies under the emission control policy. In order to achieve this goal, this paper presents a mixed-integer non-linear programming (MINP) model with two objectives and is derived from the traditional route optimization problem. The primary objective is to optimize the route and speed of a cruise liner, while simultaneously enhancing route competitiveness and minimizing carbon emissions both within and outside the SECAs. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm was used to reach the objective, and simulations were carried out to verify the effectiveness of the model and method. The results show that speed and sailing route optimization can affect carbon emissions. This paper has a certain application value and guiding significance for cruise line decision makers that will be beneficial for the environment. [ABSTRACT FROM AUTHOR]

Extensive uncertainty can affect the efficiency and fairness of consensus in the consensus reaching process (CRP), but few scholars have studied consensus modeling that focuses on fairness and efficiency in uncertain environments. Additionally, the weight of the decision maker (DM) in the CRP is influenced by multiple factors. Therefore, this paper proposes robust consensus models (EFCMs) focusing on fairness and efficiency under uncertain costs to address these issues. Firstly, this paper constructs multiple uncertainty sets to describe the uncertainty of the unit adjustment cost. Secondly, the fair utility level and opinion adjustment distance are used to measure the fairness and efficiency of reaching consensus, respectively. Furthermore, this paper uses a data-driven method based on the KDE method combined with trust propagation in social networks to determine the DMs' weights jointly. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between the government and enterprises. The experimental results and sensitivity analysis show that the consensus cost budget and the DMs' jealous preference behavior particularly affects the efficiency of reaching consensus, which provides a theoretical basis for solving practical decision making problems. [ABSTRACT FROM AUTHOR]

In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they come from many sources like different kinds of factories, firms, and industries. The burning of diesel and petrol during the supply of items through transportation is also responsible for carbon emissions. When any company, firm, or industry supplies their items through a supply chain by using of transportation in the regular mode, then a lot of carbon units are emitted from the burning of petrol and diesel, etc., which affects the supply chain. Carbon emissions can be controlled by using different kinds of policies issued by the government of a country, and lots of companies have implemented these policies to control carbon emissions. When a seller delivers a demanded lot size to the buyer, as per demand, and the lot size has some defective items, as per consideration, the demand rate is uncertain in nature. The buyer inspects the received whole lot and divides it into two categories of defective and no defective deteriorating items, as well as immediately selling at different price. The fuzzy concept nullifies the uncertain nature of the demand rate. This paper covers two models, assuming two conditions of quality screening under learning in fuzzy environment: (i) the buyer shows the quality screening and (ii) the quality inspection becomes the seller's responsibility. The carbon footprint from the transporting and warehousing the deteriorating items is also assumed. The aim of this study is to minimize the whole inventory cost for supply chains with respect to lot size and the number of orders per production cycle. Jointly optimizing the delivery lot size and number of orders per production cycle will minimize the whole fuzzy inventory cost for the supply chain and also reduce the carbon emissions. We take two numerical approaches with authentic data (from the literature reviews) for the justification of the proposed model 1 and model 2. Sensitivity observations, managerial insights, applications of these proposed models, and future scope are also included in this paper, which is more beneficial for firms, the industrial sector, and especially for online markets. The impact of the most effective parameters, like learning effect, fuzzy parameter, carbon emissions parameter, and inventory cost are shown in this study and had a positive effect on the total inventory cost for the supply chain. [ABSTRACT FROM AUTHOR]

Accurately predicting carbon dioxide (CO2) emissions is crucial for environmental protection. Currently, there are two main issues with predicting CO2 emissions: (1) existing CO2 emission prediction models mainly rely on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) models, which can only model unidirectional temporal features, resulting in insufficient accuracy: (2) existing research on CO2 emissions mainly focuses on designing predictive models, without paying attention to model optimization, resulting in models being unable to achieve their optimal performance. To address these issues, this paper proposes a framework for predicting CO2 emissions, called CGAOA-AttBiGRU. In this framework, Attentional-Bidirectional Gate Recurrent Unit (AttBiGRU) is a prediction model that uses BiGRU units to extract bidirectional temporal features from the data, and adopts an attention mechanism to adaptively weight the bidirectional temporal features, thereby improving prediction accuracy. CGAOA is an improved Arithmetic Optimization Algorithm (AOA) used to optimize the five key hyperparameters of the AttBiGRU. We first validated the optimization performance of the improved CGAOA algorithm on 24 benchmark functions. Then, CGAOA was used to optimize AttBiGRU and compared with 12 optimization algorithms. The results indicate that the AttBiGRU optimized by CGAOA has the best predictive performance. [ABSTRACT FROM AUTHOR]

In the present paper, a sustainable supply chain model is investigated with a variable production rate and remanufacturing for the production of defective items under the effect of learning fuzzy theory, where the lower and upper variations in fuzzy demand rate are affected by learning parameters and backorders are also allowed. Our proposed model reveals a springy manufacturing inventory organization that makes various types of items, and imperfect items can be created through the method of manufacturing things in a fuzzy environment. When the screening process is completed, defective items are remanufactured immediately, and a limited financial plan and space limitations are assumed concerning the product assembly. We minimized the total fuzzy inventory cost with different distributions (beta, triangular, double triangular, uniform, and χ2 (chi−square)) concerning the production rate, lot size, and backorder under learning in a fuzzy environment where the costs of screening, manufacturing, carrying, carbon emissions, backorders, and remanufacturing are included. The Kuhn–Tucker optimization technique is applied to solve non-linear equations that are based on some distributions. Numerical examples, sensitivity analysis, managerial insights and observations, limitations, future work, and applications are provided for the validation of our proposed model, and the industrial scope of this proposed work is included. [ABSTRACT FROM AUTHOR]

In the spring of 2022, a new round of epidemic broke out in Shanghai, causing a shock to the Shanghai carbon trading market. Against this background, this paper studied the impact of the new epidemic on the price of Shanghai carbon emission rights and tried to explore the prediction model under the unexpected event. First, because a model based on point value data cannot capture the information hidden in inter-day price fluctuation, based on the interval price of Shanghai carbon emission rights (SHEA) and its influencing factors, an autoregressive conditional interval model with jumping and exogenous variables (ACIXJ) was established to explore the influence of the Russian–Ukrainian conflict and COVID-19 on the interval price of SHEA, respectively. The empirical results show that the conflict between Russia and Ukraine has no obvious influence on the price of SHEA, but COVID-19 led to a decline in the price trend of SHEA over four days before the city was closed, and the volatility changed significantly on the day before the city was closed. The price fluctuation was the strongest within 3 days after the city was closed; In addition, in order to accurately predict the interval data of SHEA against the background of COVID-19, based on the interval data decomposition algorithm (BEMD), a hybrid forecasting model of NDGM-ACIXJ/CNN-LSTM was constructed, in which the discrete gray model of approximate nonhomogeneous exponential series (NDGM) combined with the ACIXJ model is used to predict the high-frequency sub-interval, and the convolution neural network long-term and short-term memory model (CNN-LSTM) is used to predict the low-frequency sub-interval. The empirical results show that the prediction model proposed in this article has higher prediction precision than the reference models (ACIX, ACIXJ, NDGM-ACIXJ, BEMD-ACIX/CNN-LSTM, BEMD-ACIXJ/CNN-LSTM). [ABSTRACT FROM AUTHOR]

This paper identifies patterns in total and per capita CO2 emissions among 208 countries considering different emission sources, such as cement, flaring, gas, oil, and coal. This research uses linear and non-linear dimensional reduction techniques, combining K-means clustering with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), which allows the identification of distinct emission profiles among nations. This approach allows effective clustering of heterogeneous countries despite the highly dimensional nature of emissions data. The optimal number of clusters is determined using Calinski–Harabasz and Davies–Bouldin scores, of five and six clusters for total and per capita CO2 emissions, respectively. The findings reveal that for total emissions, t-SNE brings together the world's largest economies and emitters, i.e., China, USA, India, and Russia, into a single cluster, while PCA provides clusters with a single country for China, USA, and Russia. Regarding per capita emissions, PCA generates a cluster with only one country, Qatar, due to its significant flaring emissions, as byproduct of the oil industry, and its low population. This study concludes that international collaboration and coherent global policies are crucial for effectively addressing CO2 emissions and developing targeted climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a sustainable inventory model with a learning effect for imperfect quality items under different kinds of fuzzy environments like crisp, general fuzzy, cloudy fuzzy, and monsoon fuzzy. We divided the mathematical model into three parts under the learning effect according to the real-time fuzzy components (crisp, cloudy, and monsoon environments) of the demand rate of the items. We minimized the total inventory cost with respect to cycle length in each environment under the proposed assumptions. The non-linear optimization technique is applied for the algorithm and the solution method to find the decision variable. Finally, we compared the total inventory cost under different fuzzy environments and our finding is that the fuzzy monsoon environment is a more effective fuzzy environment than crisp and cloudy fuzzy environments. We have presented a numerical example for the validation of the proposed model and have shown the impact of the inventory input parameters on the cycle length and total inventory fuzzy cost. The managerial insights and future scope of this proposed study have been shown in the sensitivity analysis and conclusion. The limitations, application, future extension and scope, and social implementation have been shown in this research study. [ABSTRACT FROM AUTHOR]

In order to support a low-carbon economy and manage market competition, location–inventory–routing logistics management must play a crucial role to minimize carbon emissions while maximizing customer satisfaction. This paper proposes a bi-objective mixed-integer nonlinear programming model with time window constraints that satisfies the normal distribution of stochastic customer demand. The proposed model aims to find Pareto optimal solutions for total cost minimization and customer satisfaction maximization. An improved non-dominated sorting genetic algorithm II (IMNSGA-II) with an elite strategy is developed to solve the model. The model considers cost factors, ensuring that out-of-stock inventory is not allowed. Factors such as a carbon trading mechanism and random variables to address customer needs are also included. An entropy weight method is used to derive the total cost, which is comprised of fixed costs, transportation costs, inventory costs, punishment costs, and the weight of carbon emissions costs. The IMNSGA-II produces the Pareto optimal solution set, and an entropy–TOPSIS method is used to generate an objective ranking of the solution set for decision-makers. Additionally, a sensitivity analysis is performed to evaluate the influence of carbon pricing on carbon emissions and customer satisfaction. [ABSTRACT FROM AUTHOR]

Diverse demands have promoted the rapid development of the cold chain logistics industry. In the paper, a novel approach for calculating the comprehensive carbon emission cost was proposed and the front warehouse mode was analyzed under the background of energy conservation and emission reduction. To solve the two-echelon low-carbon location-routing problem (2E-LCLRP), a mathematical model considering operating cost, total transportation cost, fixed cost, refrigeration cost, cargo damage cost, and comprehensive carbon emission cost was proposed to determine the minimum total cost. A hybrid ant colony optimization (HACO) algorithm based on an elbow rule and an improved ant colony optimization (IACO) algorithm was proposed to solve the 2E-LCLRP. According to the elbow rule, the optimal number of front warehouses was determined and an IACO algorithm was then designed to optimize vehicle routes. An adaptive hybrid selection strategy and an optimized pheromone update mechanism were integrated into the HACO algorithm to accelerate convergence and obtain global optimal solutions. The proposed model and algorithm were verified through the case study of the 2E-LCLRP in Nanjing, China. The HACO algorithm outperformed the original ant colony optimization (ACO) algorithm in terms of convergence rate and solution quality. This study provides significant insights for enhancing heuristic algorithms as well as valuable research methods. Furthermore, the results can help cold chain logistics companies in balancing economic costs and environmental benefits and address cold chain distribution of agricultural products. [ABSTRACT FROM AUTHOR]

With the development of China's express delivery industry, the number of express packaging has proliferated, leading to many problems such as environmental pollution and resource waste. In this paper, the process of reverse logistics network design for express packaging recycling is given as an example in the M region, and a four-level network containing primary recycling nodes, recycling centers, processing centers, and terminals is established. A candidate node selection model based on the K-means algorithm is constructed to cluster by distance from 535 courier outlets to select 15 candidate nodes of recycling centers and processing centers. A node selection model based on the NSGA-II algorithm is constructed to identify recycling centers and processing centers from 15 candidate nodes with minimizing total cost and carbon emission as the objective function, and a set of Pareto solution sets containing 43 solutions is obtained. According to the distribution of the solution set, the 43 solutions are classified into I, II, and III categories. The results indicate that the solutions corresponding to Class I and Class II solutions can be selected when the recycling system gives priority to cost, Class II and Class III solutions can be selected when the recycling system gives priority to environmental benefits, and Class III solutions can be selected when the society-wide recycling system has developed to a certain extent. In addition, this paper also randomly selects a sample solution from each of the three types of solution sets, conducts coding interpretation for site selection, vehicle selection, and treatment technology selection, and gives an example design scheme. [ABSTRACT FROM AUTHOR]

The present paper deals with an integrated sustainable supply chain model with the effect of learning for an imperfect production system under a cloudy fuzzy environment where the demand rate is treated as a cloudy triangular fuzzy (imprecise) number, which means that the demand rate of the items is not constant, and shortages and a warranty policy are allowed. The vendor governs the manufacturing process to serve the demand of the buyer. When the vendor supplies the demanded lot after the production of items, it is also considered that the delivery lots have some defective items that follow an S-shape learning curve. After receiving the lot, the buyer inspects the whole lot, and the buyer classifies the whole lot into two categories: one is the defective-quality items and the other is the imperfect-quality items. The buyer returns the defective-quality items to the seller after a screening process, for which a warranty cost is included. During the transportation of the items, a lot of carbon units are emitted from the transportation, damaging the quality of the environment. The seller includes carbon emission costs to achieve sustainability as per considerations. A one-time discrete investment is also included for the minimizing of the setup cost of the seller for the next cycles. We developed models for the scenario of the separate decision and for the integrated decision of the players (seller/buyer) under the model's consideration. Our aim is to jointly optimize the integrated total fuzzy cost under a cloudy fuzzy environment sustained by the seller and buyer. Numerical examples, sensitivity, analysis limitations, future scope and conclusions have been provided for the justification of the proposed model, and the impact of the input parameters on the decision variables and integrated total fuzzy cost for the supply chain are provided for the validity and robustness of this proposed model. The effect of learning in a cloudy fuzzy environment was positive for this proposed model. [ABSTRACT FROM AUTHOR]

China's policies about reducing carbon emissions mainly focus on saving energy and reducing pollutant emissions. However, the carbon emissions of the logistics industry remain high. Thus, logistics companies should not only reduce distribution costs, but also consider the impact of carbon emissions when planning distribution routes. This paper studies the supermarket delivery distribution route planning problem considering carbon emissions. Aiming at the lowest economic operating cost composed of fixed cost, driving cost, and carbon emission cost, we propose a grey wolf optimized genetic algorithm to generate delivery route plans, where the social rank and hunting behavior of the grey wolf optimization algorithm are integrated into the selection operation of the genetic algorithm. Finally, a real case of a large third-party logistics enterprise in Beijing is studied. The case study results verify the effectiveness and applicability of the model and algorithm. [ABSTRACT FROM AUTHOR]

With the global climate problem becoming increasingly severe, governments have adopted policies to encourage enterprises to invest in low-carbon technologies. However, the opacity of the carbon emission reduction process leads to incomplete consumer trust in low-carbon products as well as higher supply chain transaction costs. Based on this, this paper constructs Stackelberg game models with and without blockchain under different power structures and compares the impact of these models on low-carbon emission reduction decisions. The results show that: (1) blockchain does not necessarily improve enterprise profits and can only help enterprises maintain optimal profits within a certain range when the carbon emission cost is low; (2) when consumers' environmental awareness is high, the blockchain can incentivize manufacturers to enhance carbon emission reduction, and it has an obvious promotional effect on retailers' profits; and (3) the profit gap between enterprises in the supply chain is larger under different power structures, and the implementation of blockchain can coordinate profit distribution and narrow the gap between enterprises. Compared with the manufacturer-dominated model, the emission reduction in products is maximized under the retailer-dominated model. Our study provides theoretical support for the government to regulate greenhouse gas emissions as well as for the optimization of enterprises' decision-making supported by blockchain. [ABSTRACT FROM AUTHOR]

Aiming at the problem of multimodal transport path planning under uncertain environments, this paper establishes a multi-objective fuzzy nonlinear programming model considering mixed-time window constraints by taking cost, time, and carbon emission as optimization objectives. To solve the model, the model is de-fuzzified by the fuzzy expectation value method and fuzzy chance-constrained planning method. Combining the game theory method with the weighted sum method, a cooperative game theory-based multi-objective optimization method is proposed. Finally, the effectiveness of the algorithm is verified in a real intermodal network. The experimental results show that the proposed method can effectively improve the performance of the weighted sum method and obtain the optimal multimodal transport path that satisfies the time window requirement, and the path optimization results are better than MOPSO and NSGA-II, effectively reducing transportation costs and carbon emissions. Meanwhile, the influence of uncertainty factors on the multimodal transport route planning results is analyzed. The results show that the uncertain factors will significantly increase the transportation cost and carbon emissions and affect the choice of route and transportation mode. Considering uncertainty factors can increase the reliability of route planning results and provide a more robust and effective solution for multimodal transportation. [ABSTRACT FROM AUTHOR]

In this study, a supply chain model is proposed with preservation technology under learning fuzzy theory for deteriorating items where the demand rate depends on the selling price and also treats as a triangular fuzzy number. The deterioration rate of any item cannot be eliminated due to its natural process, but it can be controlled with the help of preservation technology. Some harmful gases are emitted during the preservation process due to deteriorating items that harm the environment. In general, it can be easily seen that most of the sellers offer a trade credit policy to their regular buyers. In this paper, the retailer's inventory stock reduces due to demand and deterioration. It is also assumed that some units are defective due to machine defects or delivery inefficiency. The retailer accepted the policy of trade credit offered by the seller. The aim of this paper is to enhance the profit of the supply chain partners. We proposed a theorem to get the optimal values of the selling price and cycle length. The retailer's total profit is a function of selling price and cycle length, and the retailer's total profit is optimized with respect to selling price and cycle length under trade-credit. Numerical examples are also presented for the validation of the present study, and sensitivity analysis is also discussed to know the robustness of the supply chain model. Managerial insight and observation have been given in the sensitivity section. Limitations and future work of this paper have been presented in the conclusion section. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) has been widely applied to evaluate the performance of banks, enterprises, governments, research institutions, hospitals, and other fields as a non-parametric estimation method for evaluating the relative effectiveness of research objects. However, the composition of its effective frontier surface is based on the input-output data of existing decision units, which makes it challenging to apply the method to predict the future performance level of other decision units. In this paper, the Slack Based Measure (SBM) model in DEA method is used to measure the relative efficiency values of decision units, and then, eleven machine learning models are used to train the absolute efficient frontier to be applied to the performance prediction of new decisions units. To further improve the prediction effect of the models, this paper proposes a training set under the DEA classification method, starting from the training-set sample selection and input feature indicators. In this paper, regression prediction of test set performance based on the training set under different classification combinations is performed, and the prediction effects of proportional relative indicators and absolute number indicators as machine-learning input features are explored. The robustness of the effective frontier surface under the integrated model is verified. An integrated models of DEA and machine learning with better prediction effects is proposed, taking China's regional carbon-dioxide emission (carbon emission) performance prediction as an example. The novelty of this work is mainly as follows: firstly, the integrated model can achieve performance prediction by constructing an effective frontier surface, and the empirical results show that this is a feasible methodological technique. Secondly, two schemes to improve the prediction effectiveness of integrated models are discussed in terms of training set partitioning and feature selection, and the effectiveness of the schemes is demonstrated by using carbon-emission performance prediction as an example. This study has some application value and is a complement to the existing literature. [ABSTRACT FROM AUTHOR]

To achieve a fundamental reduction in the carbon emissions associated with grinding machines, it is imperative to systematically explore low-carbon considerations in the design phase. The spindle is a significant contributor to carbon emissions in grinding machines, and an effective approach for reducing carbon emissions is the structural optimization of the spindle. Most of the current optimization methods aim at improving processability without considering the reduction of carbon emissions. In this context, the present study addresses the issue of carbon emissions within the spindle design phase. Initially, the determination of the spindle's carbon emissions function and the selection of the optimization objective were undertaken. The structural factors that have a significant influence on the optimization objective were identified as optimization variables. Subsequently, the optimization objective function was established through the application of the fitting method. Finally, the proposed model was refined through the utilization of an enhanced whale algorithm. The findings indicate an 8.22% reduction in carbon emissions associated with the spindle, accompanied by marginal enhancements in both static and dynamic spindle performance. The concluding section of this paper deliberates on the impact of structural parameters on the specified objectives, thereby providing insights for the optimal design of the spindle. [ABSTRACT FROM AUTHOR]

Accurate prediction of the carbon trading price (CTP) is crucial to the decision-making of relevant stakeholders, and can also provide a reference for policy makers. However, the time interval for the CTP is one day, resulting in a relatively small sample size of data available for predictions. When dealing with small sample data, deep learning algorithms can trade only a small improvement in prediction accuracy at the expense of efficiency and computing time. In contrast, fine-grained configurations of traditional model inputs and parameters often perform no less well than deep learning algorithms. In this context, this paper proposes a novel hybrid CTP prediction model based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a windowed-based XGBoost approach. First, the initial CTP data is decomposed into multiple subsequences with relatively low volatility and randomness based on the CEEMDAN algorithm. Then, the decomposed carbon valence series and covariates are subject to windowed processing to become the inputs of the XGBoost model. Finally, the universality of the proposed model is verified through case studies of four carbon emission trading markets with different modal characteristics, and the superiority of the proposed model is verified by comparing with seven other models. The results show that the prediction error of the proposed XGBoost(W-b) algorithm is reduced by 4.72%~81.47% compared to other prediction algorithms. In addition, the introduction of CEEMDAN further reduces the prediction error by 25.24%~89.28% on the basis of XGBoost(W-b). [ABSTRACT FROM AUTHOR]

Reducing carbon emissions and improving revenue in the face of global warming and economic challenges is a growing concern for airlines. This paper addresses the inefficiencies and high costs associated with current aero-engine on-wing washing strategies. To tackle this issue, we propose a reinforcement learning framework consisting of a Similar Sequence Method and a Taylor DQN model. The Similar Sequence Method, comprising a sample library, DTW algorithm, and boundary adjustment, predicts washed aero-engine data for the Taylor DQN model. Leveraging the proposed Taylor neural networks, our model outputs Q-values to make informed washing decisions using data from the Similar Sequence Method. Through simulations, we demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

A great loss of transportation capacity has been caused in auto parts supply logistics due to the independent transportation from auto parts suppliers (APSs) to the automobile production line (APL). It is believed that establishing distribution centers (DCs) for centralized collection and unified distribution is one effective way to address this problem. This paper proposes a unified framework simultaneously considering the location-inventory-routing problem (LIRP) in auto parts supply logistics. Integrating the idea of sustainable development, a multi-objective MIP model is developed to determine the location and inventory capacity of DCs and routing decisions to minimize the total system cost and carbon emissions while concerning multi-period production demand. In addition, a robust optimization model is developed further in the context of uncertain demand. Numerical experiments and sensitivity analyses are conducted to verify the effectiveness of our proposed deterministic and robust models. The results show that synergistically optimizing the location and capacity of DCs and routing decisions are beneficial in reducing total system cost and carbon emissions. The analysis can provide guidelines to decision-makers for the effective management of auto parts supply logistics. [ABSTRACT FROM AUTHOR]

Due to CO 2 emissions, humans are encountering grave environmental crises (e.g., rising sea levels and the grim future of submerged cities). Governments have begun to offset emissions by constructing emission-trading schemes (carbon-offset markets). Investors naturally crave carbon-offset options to effectively control risk. However, the research and practice for these options are relatively limited. This paper contributes to the literature in this area. Specifically, according to carbon-emission allowances' empirical distributions, we implement fractal Brownian motions and jump diffusions instead of traditional geometric Brownian motions. We contribute to extending the theoretical model based on carbon-offset option-pricing methods. We innovate the carbon-offset options of Asian styles. We authenticate the options' stochastic differential equations and analytically price the options in the form of theorems. We verify the parameter sensitivity of pricing formulas by illustrations. We also elucidate the practical implications of an emission-trading scheme. [ABSTRACT FROM AUTHOR]

This paper studies the impact of diverse carbon emission regulations on optimal operation decisions by exploiting the economic production lot model of a multi-manufacturer system in a low-carbon environment. The optimal production planning for multiple enterprises in terms of cost optimization and carbon emission optimization are presented in various scenarios of the fully decentralized system under carbon taxation, cap and trade, and mandatory carbon cap. We prove a unified framework for modeling the scenarios enabled by carbon tax and cap-and-trade policies. Furthermore, the decision vector of the optimal production lot is obtained by analyzing the joint decision scenario based on shared carbon emission caps. We give a comparative analysis of two settings of fully decentralized and carbon quota sharing. The results show that the decision of joint production and stock preparation can reduce the total operational costs of the multi-enterprise system, but the realized carbon emissions may not change or only increase slightly. Our study provides a useful reference for government carbon emission regulation and enterprise operation decision optimization under carbon neutrality and a carbon peak environment. [ABSTRACT FROM AUTHOR]

This paper considers a fresh food supply chain with a supplier who takes responsibility for the cold chain and a retailer who needs to reprocess the fresh food. Carbon emissions will be produced in the processes of production, transportation, processing, etc. We consider the four-stage game, obtain the function expressions of optimal market prices with respect to carbon emission reduction level (CERL), analyze the best responses of the supplier and the retailer regarding their CERLs, and obtain the 25 optimal CERLs under competitive equilibrium. In 24 of the 25 equilibrium cases, the supplier or the retailer either do nothing to reduce carbon emissions, or make the most effort to reduce carbon emissions. Excluding these special cases, we focused on a non-trivial case where the increasing consumer preferences for low-carbon products will encourage the supplier and the retailer to reduce carbon emissions. Interestingly, we find that when the consumer preference for low-carbon products is low, the retailer's and supplier's equilibrium carbon reduction levels are low, so that the potential market size is small such that the competition for two kinds of customers is fierce. Then, an increase in the sale cost will reduce the retail price. However, when the consumer preference for low-carbon products is high, the potential market size is large such that the competition is not fierce. Then, an increase in the sale cost will advance the retail price. [ABSTRACT FROM AUTHOR]

The present consumer behavior is manipulated by "fast fashion", where purchasing new, trendy, affordable clothes is preferred over recycling old ones. This changing mannerism has escalated the GHG emissions from the fashion industry. Energy-intensive raw material production, preparation, and processing contribute to considerable emissions. The management of the returned goods from the primary market and further processing through the secondary outlets indulge in reverse logistics. In this paper, efforts are made to minimize the total cost and the carbon emission amount during the process of managing the return articles from the primary market to the reverse distribution center, further processing of the articles at the secondary outlet, and the return of the unsold or excess articles from the secondary outlet. Reverse cross-docking has been implemented in managing the return articles, while environmental concerns over GHG emissions have been addressed by investing in green technology under a strict carbon cap policy. In this research, return articles from the primary and secondary markets, rework of the returned articles, and disposal of the impaired returned articles have been considered. The carbon emission cost at all stages of transportation, rework, or disposal has also been incorporated into this model. A constrained mixed integer linear programming model is proposed and solved considering green investment. A numerical example has been formulated to investigate the effect of green technology on the total cost. The results portray that, though the total cost increases by nearly 2% due to investment in green technology, it ensures a considerable drop of 23% in the carbon emission amount. Also, the result is successful in establishing that reverse cross-docking is a better option than traditional warehousing in terms of minimizing the cost. [ABSTRACT FROM AUTHOR]

This paper aims to propose a comprehensive inventory model including pricing, pre-sale incentives, advance sales, trade credit, and carbon tax policies. The novelty of this study lies in its holistic approach to addressing these relevant and practical issues. The major purpose is to determine the optimal pricing, pre-order discount, and replenishment decisions to maximize the total profit under carbon tax policy. Through theoretical analysis, this study develops several theorems to demonstrate properties of optimal solutions and an easy-to-use algorithm to derive optimal solutions. Further, several numerical examples are provided to demonstrate the solution process for different scenarios and the effects of various parameters on optimal alternatives and solutions. This study provides companies management implications to address the challenges posed by the global movement to reduce carbon emissions while maintaining their profitability. [ABSTRACT FROM AUTHOR]

With the release of the national energy-saving emission reduction policy and the improvement of consumers' awareness of environmental protection, the demand for low-carbon products is growing rapidly. In a retailer-led low-carbon supply chain, the increased cost of carbon emission reduction puts manufacturers at a disadvantage. Under the carbon quota policy, to improve manufacturers' profits as well as enhance carbon emission reduction, this paper studies the players' decisions in a low-carbon supply chain consisting of one dominant retailer and one manufacturer. To maintain the supply chain's stability and sustainability, the dominant retailer tends to employ altruistic preference policies towards the manufacturer. The optimal decision, carbon emission reduction and supply chain profit are compared and analyzed under three decision models: (i) centralized decision, (ii) decentralized decision without altruistic preference and (iii) decentralized decision with altruistic preference. The results indicate that the carbon emission reduction rate, market demand and profit in the centralized model are higher than in the decentralized model. The retailer's altruistic preference is beneficial to the improvement of carbon emission reduction, market demand and the profit of the manufacturer and the supply chain. Under certain conditions, carbon trading can effectively reduce the cost pressure of manufacturers and improve the level of carbon emission reduction and the overall profit of the supply chain. These results will guide low-carbon supply chain decision-making and provide insight into the research of irrational behaviors in supply chain decision-making under carbon policies. [ABSTRACT FROM AUTHOR]

Assuming the significance of sustainability, it is considered necessary to ensure the conservation of our natural resources, in addition to minimizing waste. To promote significant sustainable effects, factors including production, transportation, energy usage, product control management, etc., act as the chief supports of any modern supply chain model. The buyer performs the firsthand inspection and returns any defective items received from the customer to the vendor in a process that is known as first-level inspection. The vendor uses the policy of recovery product management to obtain greater profit. A concluding inspection is accomplished at the vendor's end in order to distinguish the returned item as belonging to one of four specific categories, namely re-workable, reusable, recyclable, and disposable, a process that is known as second-level inspection. Then, it is observed that some defective items are suitable for a secondary market, while some are reusable, and some can be disassembled to shape new derived products, and leftovers can be scrapped at the disposal cost. This ensures that we can meet our target to promote a cleaner drive with a lower percentage of carbon emissions, reducing the adverse effects of landfills. The activity of both players in this model is presented briefly in the flowchart shown in the abstract. Thus, our aim of product restoration is to promote best practices while maintaining economic value, with the ultimate goal of removing the surrounding waste with minimum financial costs. In this regard, it is assumed that the demand rate is precise in nature. The learning effect and fuzzy environment are also considered in the present model. The proposed model studies the impacts of learning and carbon emissions on an integrated green supply chain model for defective items in fuzzy environment and shortage conditions. We optimized the integrated total fuzzy profit with respect to the order quantity and shortages. We described the vendor's strategy and buyer's strategy through flowcharts for the proposed integrated supply chain model, and here, in the flowchart, R-R-R stands for re-workable, reusable, and recyclable. The demand rate was treated as a triangular fuzzy number. In this paper, a numerical example, sensitivity analysis, limitations, future scope, and conclusion are presented for the validation of the proposed model. [ABSTRACT FROM AUTHOR]

This paper studies a green manufacturer's strategic licensing of its green technology to a brown rival under the consideration of the green manufacturer's environmental concerns. Consumers in the market have green awareness. Adopting the green technology not only helps manufacturers to reduce carbon emissions, but also to increase market sizes. The green manufacturer can choose from three technology licensing strategies, i.e., no licensing (N), royalty licensing (R) or fixed-fee licensing (F). The equilibrium licensing strategy can be derived by comparing the respective payoff after adopting the three strategies. It is found that the green manufacturer should choose fixed-fee licensing strategy when the market size expansion effect is strong, and the competition intensity is moderate; otherwise, the green manufacturer should choose royalty licensing strategy. Furthermore, it is found that when the green manufacturer is more concerned about environmental impacts, it will be more willing to choose fixed-fee licensing strategy, rather than royalty licensing strategy. Through numerical tests, some interesting results are also found. For example, the brown manufacturer might be hurt even if the cost reduction effect of technology licensing is relatively stronger. Moreover, it is found that consumer surplus and social welfare nonmonotonically change with the substitution level. In summary, this research tries to provide some guidelines to the industry and the society on better managing green technology diffusions. [ABSTRACT FROM AUTHOR]

Global warming has brought about enormous damage, therefore, some scholars have begun to conduct in-depth research on peak carbon dioxide emissions and carbon neutrality. In this paper, based on the background of China's upgrading industrial structure and energy structure, we establish a delayed two-dimensional differential equation model associated with China's adjustment of industrial structure. Firstly, we analyze the existence of the equilibrium for the model. We also analyze the characteristic roots of the characteristic equation at each equilibrium point for the model, then, we analyze the stability of the equilibrium point for the model according to the characteristic root, and discuss the existence of Hopf bifurcation of the system by using bifurcation theory. Secondly, we derive the normal form of Hopf bifurcation by using the multiple time scales method. Then, through the official real data, we present the range of some parameters in the model, and determine a set of parameters by reasonable analysis. The validity of the theoretical results is verified by numerical simulations. Finally, we use the real data to forecast the time of peak carbon dioxide emissions and carbon neutralization. Eventually, we put forward some suggestions based on the current situation of carbon emission and absorption in China, such as planting trees to increase the growth rate of carbon absorption, deepening industrial reform and optimizing energy structure to reduce carbon emissions. [ABSTRACT FROM AUTHOR]

The increase of carbon emissions accelerates global warming and causes environmental problems. Therefore, research on emission peak and carbon neutrality is of broad interest. In this paper, based on the relationships among carbon emissions, carbon absorption and urban population growth, a delayed differential equations model is established. Then, we analyze the existence and stability of a positive equilibrium point of the system and substitute the data for simulations. Eventually, according to theoretical analysis and numerical simulations, some suggestions for China to achieve the goal of emission peak and carbon neutrality are given. [ABSTRACT FROM AUTHOR]

Under the background of a circular economy, this paper examines multi-tiered closed-loop supply chain network competition under carbon emission permits and discusses how stringent carbon regulations influence the network performance. We derive the governing equilibrium conditions for carbon-capped mathematical gaming models of each player and provide the equivalent variational inequality formulations, which are then solved by modified projection and contraction algorithms. The numerical examples empower us to investigate the effects of diverse carbon emission regulations (cap-and-trade regulation, mandatory cap policy, and cap-sharing scheme) on enterprises' decisions. The results reveal that the cap-sharing scheme is effective in coordinating the relationship between system profit and carbon emission abatement, while cap-and-trade regulation loses efficiency compared with the cap-sharing scheme. The government should allocate caps scientifically and encourage enterprises to adopt green production technologies, especially allowing large enterprises to share carbon quotas. This study can also contribute to the enterprises' decision-making and revenue management under different carbon emissions reduction regulations. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions plays a significant role in developing sustainable inventory systems. In a seller-buyer relationship, an allowable delay in payment is considered for the buyer to manage the stock and simulate the demand. Deteriorating items that usually have specific maximum lifetimes have become a challenge for most firms. Contrary to the importance of these issues, very little research has studied the impact of carbon emissions on deteriorating inventory systems. This paper provides a price-dependent demand for perishable items when carbon cap-and-trade regulation fills the mentioned gap. This model provides a carbon reduction investment scheme and illustrates this investment's effect on the inventory system. This paper determines the optimal replenishment cycle and selling price, in which: (a) perishable items have specific maximum lifetimes, (b) a specific period of delay in payment is allowed for the buyer to accumulate revenue, (c) carbon is emitted due to ordering and storage operations and carbon cap and trade is regulated along with allowable carbon reduction investment. After developing the model, optimal values are obtained from necessary and sufficient conditions of optimality. Numerical experiments are proposed to validate the model. By developing an algorithm, the optimal values of replenishment cycle, selling price, and carbon reduction technology investment are obtained, and the impact of carbon emissions and efforts to control emissions are outlined. Finally, some managerial applications are mentioned, and future research directions are exposed. [ABSTRACT FROM AUTHOR]

Annually, over 10 billion tons of construction and demolition waste is transported globally from sites to reception facilities. Optimal and effective planning of waste transportation holds the potential to mitigate cost and carbon emissions, and alleviate road congestion. A major challenge for developing an effective transportation plan is the uncertainty of the precise volume of waste at each site during the planning stage. However, the existing studies have assumed known demand in planning models but the assumption does not reflect real-world volatility. Taking advantage of the problem structure, this study adopts the stochastic programming methodology to approach the construction waste planning problem. An integer programming model is developed that adeptly addresses the uncertainty of the amount of waste in an elegant manner. The proposed stochastic programming model can efficiently handle practical scale problems. Our numerical experiments amass a comprehensive dataset comprising nearly 4300 records of the actual amount of construction waste generated in Hong Kong. The results demonstrate that incorporating demand uncertainty can reduce the transportation cost by 1% correlating with an increase in profit of 14% compared to those that do not consider the demand uncertainty. [ABSTRACT FROM AUTHOR]

To address the growing demand for green development, governments worldwide have introduced policies to promote a green economy. Among these policies, the carbon cap-and-trade mechanism is adopted as an effective approach to control carbon emissions. Additionally, blockchain may increase transparency in the industrial process. Despite focusing on improving its own green standards, the supply chain needs to establish stable cooperative relationship. Thus, we focus on a supply chain consisting of a dominant manufacturer and a retailer, where the manufacturer opts for implementing blockchain and the retailer selects their stance on fairness. We construct a Stackelberg game model and use backward induction to obtain the equilibrium solutions. In the supply chain, the highest profits can be achieved when the manufacturer adopts blockchain technology, provided that the cost of application is relatively low. For manufacturer and retailer, when the cost of applying blockchain is relatively low, they can both obtain maximized profits without applying blockchain and the retailer does not have fairness concerns. However, as the cost of inducing blockchain and the product's reduction in carbon emission increase, the optimal strategies for manufacturer and retailer begin to diverge, which may affect the stability of the supply chain. [ABSTRACT FROM AUTHOR]

Competition in China's electric vehicle industry has intensified significantly in recent years. The production mode of power batteries, serving as the pivotal component in these vehicles, has emerged as a critical challenge for electric vehicle manufacturers. We considered a system comprising an electric vehicle (EV) manufacturer with power battery production technology and another EV manufacturer lacking power battery production technology. In the context of carbon trading policy, we constructed and solved Cournot competitive game models and asymmetric Nash negotiation game models in the CC, PC, and WC modes. We examined the decision-making process of electric vehicle manufacturers regarding power battery production modes and carbon emission reduction strategies. Our research indicates the following: (1) The reasonable patent fee for power batteries and the wholesale price of power batteries can not only compensate power battery production technology manufacturers for the losses caused by market competition but can also strengthen the cooperative relationship between manufacturers. (2) EV manufacturers equipped with power battery production technology exhibit higher profitability within the framework of a perfectly competitive power battery production mode. Conversely, manufacturers lacking power cell production technology demonstrate greater profitability when operating under a more collaborative power cell production mode. (3) Refraining from blindly persisting with and advocating for carbon emission reduction measures is advisable for manufacturers amidst rising carbon trading prices. [ABSTRACT FROM AUTHOR]

Climatic change and environmental degradation have become a worldwide discourse. Green innovation is commonly viewed as a means of lowering environmental pollution in the era of climate change. Considering this, the primary purpose of this study is to investigate the effects of environmental technology (ET) on CO2 emissions by controlling Saudi Arabia's ICT use, energy use, energy intensity, and financial development. It uses a quantile-based multiple-threshold "nonlinear autoregressive distributed lag (NARDL)" estimation utilizing data from 1990 to 2020. It also conducts the ARDL and NARDL estimation techniques simultaneously for comparative outcomes. The Toda–Yamamoto (T-Y) causality assessment also crosschecks the primary multiple-threshold NARDL estimates. The outcomes reveal that ET promotes environmental pollution due to its low scale compared to the Kingdom's technological base. ICT improves environmental quality, and energy consumption deteriorates it. All three estimation techniques confirm these findings. The multiple-threshold NARDL estimation appears robust and reveals damaging impacts of energy intensity and financial development on emissions. The T-Y causality assessment also authenticates the primary estimation outcomes. The outcomes have important implications for policymakers to focus on enhancing patents for ET, raising ICT diffusion, reducing energy intensity through generating more renewable energies, expanding financial support for ICT and green investments, and ensuring a sustainable environment. [ABSTRACT FROM AUTHOR]

As the environment of the Internet of Things (IoT) gradually becomes common and mature, various smart application (APP) platforms have sprung up, making what we are doing more convenient, more economical and more efficient. Then, this paper used a bank APP as the research background to discuss issues related to smart APPs. Obviously, through the bank APPs, customers can complete their transfer and payment for various expenses at home, eliminating the inconvenience of going out, which not only can alleviate traffic congestion as well as reduce carbon emissions but also can save the manpower expenditure costs for banks. Consequently, improving APP performance and increasing the number of users of an APP is a very important issue. Therefore, this paper proposed an APP performance index to evaluate the performance of a bank APP. This APP performance index is to evaluate the performance of the APP through the time interval of customers' access to the APP. The shorter the time interval is, the greater the number of users within a unit time is. In addition, based on cost considerations and effectiveness, the sample size n is usually not too large in practice, in order to make decisions quickly and accurately in a short time. Since the fuzzy testing model based on the confidence interval can be integrated with the past accumulated experience of data experts, the testing accuracy can be leveled up under the condition of small-sized samples. Accordingly, a fuzzy evaluation model was proposed to evaluate whether the performance of the bank APP can reach the required level, and this model was also regarded as a basis for decision-making to determine whether to improve the bank APP. At the same time, we can grasp the opportunities for improvement, achieve the effect of cost reduction, energy saving and carbon reduction, and further move towards the goal of innovative and intelligent management. [ABSTRACT FROM AUTHOR]

Disassembly lines are widely utilized to disassemble end-of-life products. Most of the research focuses on the complete disassembly of obsolete products. However, there is a lack of studies on profit and on carbon emission saved. Hence, this study considers the multi-objective partial disassembly line balancing problem with AND/OR precedence relations to optimize profit, saved carbon emission and line balance simultaneously. Firstly, a multi-objective mixed-integer programming model is formulated, which could optimally solve the small number of instances with a single objective. Meanwhile, an improved multi-objective artificial bee colony algorithm is developed to generate a set of high-quality Pareto solutions. This algorithm utilizes two-layer encoding of the task permutation vector and the number of selected parts, and develops two-phase decoding to handle the precedence relation constraint and cycle time constraint. In addition, the modified employed bee phase utilizes the neighborhood operation, and the onlooker phase utilizes the crossover operator to achieve a diverse population. The modified scout phase selects a solution from the Pareto front to replace the abandoned individual to obtain a new high-quality solution. To test the performance of the proposed algorithm, the algorithm is compared with the multi-objective simulated annealing algorithm, the original multi-objective artificial bee colony algorithm and the well-known fast non-dominated genetic algorithm. The comparative study demonstrates that the proposed improvements enhance the performance of the method presented, and the proposed methodology outperforms all the compared algorithms. [ABSTRACT FROM AUTHOR]

In the supplier–retailer–consumer system, the retailer's replenishment and pricing strategies impact the entire transaction process, forming a comprehensive trading market. Suppliers offer advance-cash-credit payments to retailers, while retailers provide customers with cash-credit payment options. In the current health-conscious consumer market, purchasing decisions are influenced not only by commodity prices but also by the freshness of products, particularly perishable goods. Growing awareness of climate change and the advent of carbon emission policies have raised concerns about the environmental costs of business transactions. This study focuses on perishable products whose demand is influenced by both price and freshness. It explores the adoption of various payment methods by suppliers and retailers, as well as the impact of carbon emission cap-and-trade policies or carbon tax policies on management and pricing strategies. Suitable inventory models are established to determine the optimal replenishment and pricing strategies for maximizing the current value of total profit. We illustrate that the current value of total profit demonstrates joint concavity concerning both the selling price and the replenishment time. Finally, we verify the proposed models using numerical examples and present the findings of sensitivity analyses. The findings of this study yield several valuable insights for inventory management of perishable goods. [ABSTRACT FROM AUTHOR]

Global emphasis on sustainable development is widespread, with industries playing a pivotal role in advancing global sustainability within the business and retail sectors. Consumer awareness of environmental concerns, such as pollution, prompts a focus on product biodegradability and eco-friendliness. Consequently, customers are drawn to products with higher green credentials. This study delves into the effectiveness of green attributes in retail industries, exploring the optimization of profit through a variable production rate and variable unit production cost, considering the selling price and the demand dependent on the product's green level. In the long run, production systems may shift to an "out-of-control" state, resulting in the random production of imperfect items that must be remanufactured to maintain the industry's positive brand image. To mitigate the impact of defective items, the industry opts to partially outsource a percentage of items, preventing shortages. However, this complex retailing system generates a significant amount of carbon emissions. This study introduces investments aimed at reducing carbon emissions to address this issue. In contrast with the existing literature, a green-level-dependent unit raw material cost is considered here for variable unit production cost. Ultimately, this study seeks to maximize the overall system's profit by optimizing the selling price, order quantity, production rate, green level, and carbon emission reduction investments. The classical optimization technique is utilized to obtain analytic optimum results for the decision variables and total profit. Special cases and sensitivity analyses illustrate the real-world applicability and impact of green levels. Numerical findings indicate that considering the product's green-level-dependent demand and unit production rate is 22.44 % more beneficial than nongreen products, partial outsourcing provides a 1.28 % advantage, and flexibility in the production rate yields a 69.60 % benefit over traditional systems without green elements. Additionally, technological investments to reduce carbon emissions result in a notable reduction of up to 4.53 % . [ABSTRACT FROM AUTHOR]

The proposed study presents an economic lot size and production rate model for a single vendor and a single buyer setup. This model involves greenhouse gas (GHG) emissions from industrial sources. The carbon emissions in this model are considered as two types: direct emissions and indirect emissions. The production rate affects carbon emissions generation in production, i.e., generally, higher production rates result in more emissions, which is governable in many real-life cases. The production rate also impacts the process reliability and quality. Faster production deteriorates the production system quickly, leading to machine failure and defective items. Such reliability and quality problems increase energy consumptions and supply chain (SC) costs. This paper formulates a vendor-buyer SC model that tackles these issues. It considers two decision-making policies: integrated or centralized as well as decentralized, where the aim is to obtain the optimal values of the decision variables that give the minimum total SC cost. It includes the costs of setup, holding inventory, carbon emissions, order processing, production, reworking, and inspection processes. The decision variables are the production rate, lead time, order quantity, the number of shipments, and the investments for setup cost reduction. In the later sections, this paper compares the numerical outcomes of the two centralized and decentralized policies. It also provides sensitivity analysis and useful insights on the economic and environmental execution of the SC. [ABSTRACT FROM AUTHOR]

The greening trend in consumer markets and the marketization and financialization of carbon emission rights have begun to revitalize carbon assets. However, solitary efforts and the spillover of environmental protection effects still hamper enterprises' enthusiasm for carbon emission reduction. To tackle this challenge, two vertical cooperation mechanisms, cost cooperation and alliance cooperation, are proposed. The mathematical models and solutions are developed for both of the two mechanisms, and their values and applicability are explored, respectively. In addition, the impact of fluctuations in consumer markets, financial markets, and carbon markets on cooperation is examined. The results show that both cooperation models effectively motivate enterprises to enhance carbon reduction and boost market demand. However, cost cooperation may result in inflated product prices and even weaken the profitability of the supply chain. In contrast, alliance cooperation can enhance product price performance and effectively increase supply chain profits. Concerning environmental performance, the initial market is better suited for alliance cooperation, whereas cost cooperation fits the mid-to-late market. The higher financing costs of the financial market and the trading price of the carbon market will strengthen the applicability of cost cooperation. This study offers managerial insights for collaborative decision-making in the context of a multi-market cross-section. [ABSTRACT FROM AUTHOR]

Over the years, the vehicle routing problem has been studied by several authors, creating several extensions, such as the multi-compartment vehicle routing problem. Several studies in the literature have addressed this problem, but few have solved it through exact approaches owing to model convolution. In this way, a mathematical model is proposed for the multi-compartment vehicle routing problem with time windows, in which three types of fuel products are distributed to a set of customers using a limited homogeneous fleet. The model explicitly considers time windows, as well as regulatory rest times for the drivers and time limits for each trip and for working schedules, addressing a real company's decision support requirements, which is scarce in the literature. The optimal solution determines, for each vehicle, the distribution route and time to carry out the deliveries with the corresponding loading of products to compartments, complemented by the calculation of carbon emissions. The main objective is to minimize the total distance traveled, which corresponds to the sum of the distances traveled by each one of the allocated vehicles. The results allow the assessment of the solution optimization applied to a set of instances for a Portuguese company to evaluate the performance and compare decision support improvements with current baseline company procedures. [ABSTRACT FROM AUTHOR]

In order to tackle sulfur oxides ( SO x ) emissions from maritime activities, both local governmental bodies and the International Maritime Organization (IMO) have implemented a range of regulations with the establishment of sulfur emission control areas (SECAs) being one crucial measure. Recently, the IMO made the significant decision to designate the Mediterranean as an SECA, aiming to promote environmental conservation as well as sustainable development in the maritime industry and mitigate the adverse health effects caused by air pollutants emitted from ships in Mediterranean regions. While this policy signifies significant progress in the reduction of sulfur emissions, it simultaneously presents intricate challenges for maritime enterprises. Notably, under the Mediterranean SECA designation, shipping companies may opt to bypass this region and choose routes through the Cape of Good Hope as a means of minimizing the overall costs, resulting in a potential increase in global carbon emissions. To support shipping companies in formulating optimal strategies within the framework of this new policy, the research introduces advanced techniques to make the optimal decisions concerning route selection, sailing speeds, and the appropriate number of ships for both SECAs and non-SECAs. Furthermore, we elucidate how these optimal decisions can be dynamically adapted in response to the dynamic fluctuations in fuel prices and the weekly operational expenditures incurred by maritime fleets. In the experimental results, taking into account factors like route distance and fuel costs, shipping companies select routes through the Mediterranean region in both eastward and westward directions. The total cost amounts to $6,558,766.78, utilizing eight vessels. Regarding ship speeds, vessels sail at reduced speeds in SECAs compared to non-SECAs. Furthermore, longer voyage distances require deploying a greater number of ships to maintain a weekly service frequency. This research exhibits robust timeliness and practicality, which is in line with practice. It not only timely supplements and enhances the extant body of knowledge concerning SECAs but also serves as a valuable point of reference and emulation for shipping companies seeking to optimize their operations within the framework of the new policy landscape. Furthermore, it offers pertinent insights for the IMO in formulating policies related to SECAs. [ABSTRACT FROM AUTHOR]

The modern logistics industry in relation to economic growth and carbon emission has opened new strategic perspectives. Recent research work have analyzed such complex interference from a broad perspective. However, analyzing this overlap needs comprehensive insight into the logistics industry while simultaneously estimating its short-run and long-run effects from regional aspects due to continue-evolving factors and their impact on it. This paper competently analyzes logistics industry components in connection with economic prosperity, energy consumption, trade development, and carbon emission from a more specific regional perspective of a developing country. Methodologically, an autoregressive distributive lag model (ARDL) is employed using correlative evaluation of the dynamic factors and their interactive impact in short and long run on this relation, based on time-series data of Pakistan from 1990 to 2019. The study results endorse the previous studies' outcomes by recognizing that an increase in carbon emission depends on trade development, energy usage, economic development, and the logistics industry's various components except for air logistics. However, study results show a unidirectional long-run causality directing from economic development, logistics industry, energy utilization, and trade development to carbon emission. Moreover, these results reveal that this emission is the leading factor to introduce stringent emission standards that further overlap with regional demographics trends, i.e., carbon emission implications. These findings imply that economic development applies a substantial demand-pull impact on national logistics, i.e., regional economic development directs to the growth of the logistics industry in the corresponding region. Consequently, high-income geographical regions have higher long-run risk concerning contemporary developmental activities of the logistics industry when adhering to carbon emission standards. Particularly, the influence of upcoming emission standards must be prioritized when planning the future returns of contemporary research and development activities of the logistics industry in a given geographic area, such as CPEC. Given Pakistan's perspective, the proposed empirical analysis can be exampled to other developing countries. This analysis may facilitate the design and development of strategies for upcoming financial funding in the modern logistics industry to seek its sustainable development-goals in developing economies. [ABSTRACT FROM AUTHOR]

The recycling of waste products can bring enormous economic and environmental benefits to supply chain participants. Under the government's reward and punishment system, the manufacturing industry is facing unfolded pressure to minimize carbon emissions. However, various factors related to the design of closed-loop logistics networks are uncertain in nature, including demand, facility capacity, transportation cost per unit of product per kilometer, landfill cost, unit carbon penalty cost, and carbon reward amount. As such, this study proposes a new fuzzy programming model for closed-loop supply chain network design which directly relies on fuzzy methods based on the necessity measure. The objective of the proposed optimization model is to minimize the total cost of the network and the sum of carbon rewards and penalties when selecting facility locations and transportation routes between network nodes. Based on the characteristics of the problem, a genetic algorithm based on variant priority encoding is proposed as a solution. This new solution encoding method can make up for the shortcomings of the four traditional encoding methods (i.e., Prüfer number-based encoding, spanning tree-based encoding, forest data structure-based encoding, and priority-based encoding) to speed up the computational time of the solution algorithm. Several alternative solution approaches were considered to evaluate the proposed algorithm including the precision optimization method (CPLEX) and priority-based encoding genetic algorithm. The results of numerous experiments indicated that even for large-scale numerical examples, the proposed algorithm can create optimal and high-quality solutions within acceptable computational time. The applicability of the model was demonstrated through a sensitivity analysis which was conducted by changing the parameters of the model and providing some important management insights. When external parameters change, the solution of the model maintains a certain level of satisfaction conservatism. At the same time, the changes in the penalty cost and reward amount per unit of carbon emissions have a significant impact on the carbon penalty revenue and total cost. The results of this study are expected to provide scientific support to relevant supply chain enterprises and stakeholders. [ABSTRACT FROM AUTHOR]